plot.GeDSgam: Plot Method for GeDSgam Objects

In GeDS: Geometrically Designed Spline Regression

Plot Method for GeDSgam Objects

Description

Plots on the linear predictor scale the component functions of a GeDSgam object fitted using NGeDSgam.

Usage

## S3 method for class 'GeDSgam'
plot(x, base_learners = NULL, f = NULL, n = 3L, ...)

Arguments

x	A GeDSgam object, as returned by NGeDSgam().
base_learners	Either NULL or a vector of character string specifying the base-learners of the model for which predictions should be plotted. Note that single base-learner predictions are provided on the linear predictor scale.
f	(Optional) specifies the underlying component function or generating process to which the model was fit. This parameter is useful if the user wishes to plot the specified function/process alongside the model fit and the data.
n	Integer value (2, 3 or 4) specifying the order (= degree + 1) of the GAM-GeDS fit.
...	Further arguments to be passed to the plot.default function.

See Also

NGeDSgam

Examples

## Gu and Wahba 4 univariate term example ##
# Generate a data sample for the response variable
# y and the covariates x0, x1 and x2; include a noise predictor x3
set.seed(123)
N <- 400
f_x0x1x2 <- function(x0,x1,x2) {
  f0 <- function(x0) 2 * sin(pi * x0)
  f1 <- function(x1) exp(2 * x1)
  f2 <- function(x2) 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10
  f <- f0(x0) + f1(x1) + f2(x2)
  return(f)
}
x0 <- runif(N, 0, 1)
x1 <- runif(N, 0, 1)
x2 <- runif(N, 0, 1)
x3 <- runif(N, 0, 1)
# Specify a model for the mean of y
f <- f_x0x1x2(x0 = x0, x1 = x1, x2 = x2)
# Add (Normal) noise to the mean of y
y <- rnorm(N, mean = f, sd = 0.2)
data <- data.frame(y = y, x0 = x0, x1 = x1, x2 = x2, x3 = x3)

# Fit GAM-GeDS model
Gmodgam <- NGeDSgam(y ~ f(x0) + f(x1) + f(x2) + f(x3), data = data)

f0 <- function(x0) 2 * sin(pi * x0)
f1 <- function(x1) exp(2 * x1)
f2 <- function(x2) 0.2 * x2^11 * (10 * (1 - x2))^6 + 10 * (10 * x2)^3 * (1 - x2)^10
fs <- list(f0, f1, f2)
main_f0 <- expression(
  f[0](x[0]) == 2 * sin(pi * x[0])
  )
main_f1 <- expression(
  f[1](x[1]) == e^(2 * x[1])
  )
main_f2 <- expression(
  f[2](x[2]) == 0.2 * x[2]^11 * (10 * (1 - x[2]))^6 +
    10 * (10 * x[2])^3 * (1 - x[2])^10
  )
mains <- list(main_f0, main_f1, main_f2)

# Create and display the plot
par(mfrow = c(1,3), mar = c(5.1, 5.1, 4.1, 2.1))
for (i in 1:3) {
  # Plot the base learner
  plot(Gmodgam, n = 3, base_learners = paste0("f(x", i-1, ")"), f = fs[[i]],
  main = mains[[i]], col = "seagreen",
  cex.lab = 2, cex.axis = 2, cex.main = 1.5)
# Add legend
if (i == 2) {
  position <- "topleft"
} else if (i == 3) {
  position <- "topright" 
} else {
  position <- "bottom"
}  
  legend(position, legend = c("NGeDSgam quad.", paste0("f(x", i-1, ")")),
         col = c("seagreen", "darkgray"),
         lwd = c(2, 2),
         bty = "n",
         cex = 1.5)
}

GeDS documentation built on June 30, 2025, 9:07 a.m.